Residual Parameter Transfer for Deep Domain Adaptation

CVPR 2018
Pages: 4339 - 4348
Published: Jun 1, 2018
Abstract
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representations that are invariant to the changes that occur when going from one domain to the other, which means using the same network parameters in both domains. While some recent algorithms explicitly...
Paper Details
Title
Residual Parameter Transfer for Deep Domain Adaptation
Published Date
Jun 1, 2018
Journal
Pages
4339 - 4348
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.